Abstract: Data protection operations including replication operations that dynamically adapt a topology of replica virtual machines. A data protection system may implement a machine model that is trained using, as input, characteristics of virtual machines. When a failure is predicted, a topology of the replica virtual machines is changed. The topology may also change when changes in the environment are detected. The changes may include redistributing the protected applications to the replica virtual machines and/or scaling the replica virtual machines.

Abstract: Systems and techniques are provided for monitoring and managing the performance of services accessed by sites on a computing platform. When a performance issue is identified, a service is monitored to determine if calls to the service exceed a threshold completion time. If so, a resource available to call the service is adaptively throttled by the platform.

Abstract: A system control processor manager for performing workloads using composed information handling systems instantiated using information handling systems includes persistent storage and a workload manager. The workload manager obtains performance information for a workload of the workloads; makes a determination, based on the performance information, to suspend performance of the workload; in response to the determination: deallocates a first computing resource of a composed information handling system performing the workload to a reserved pool; and deallocates a second computing resource of the composed information handling system to a freely allocable pool.

Abstract: Fast modern interconnects may be exploited to control when garbage collection is performed on the nodes (e.g., virtual machines, such as JVMs) of a distributed system in which the individual processes communicate with each other and in which the heap memory is not shared. A garbage collection coordination mechanism (a coordinator implemented by a dedicated process on a single node or distributed across the nodes) may obtain or receive state information from each of the nodes and apply one of multiple supported garbage collection coordination policies to reduce the impact of garbage collection pauses, dependent on that information. For example, if the information indicates that a node is about to collect, the coordinator may trigger a collection on all of the other nodes (e.g., synchronizing collection pauses for batch-mode applications where throughput is important) or may steer requests to other nodes (e.g., for interactive applications where request latencies are important).

Abstract: A method and a supporting node (150) for supporting a process scheduling node (110) when scheduling a process to a first execution node (130) of a cluster (120) of execution nodes (130, 140, 150) are disclosed. The supporting node (150) receives (A140), from the first execution node (130) being selected by the process scheduling node (110) for execution of the process, a request for allocation of one or more HA devices (131, 141, 151). The supporting node (150) allocates at least one HA device (141), being associated with a second execution node (140) of the cluster (120), to the first execution node (130). The supporting node (150) reduces a value representing number of HA devices (131, 141, 151) available for allocation to the first execution node (130) while taking said at least one HA device (141) into account. The supporting node (150) sends the value to the first execution node (130).

Abstract: A method for scheduling tasks from a program executed by a multi-processor core system is disclosed. The method includes a scheduler that groups a plurality of tasks, each having an assigned priority, by priority in a task group. The task group is assembled with other task groups having identical priorities in a task group queue. A hierarchy of task group queues is established based on priority levels of the assigned tasks. Task groups are assigned to one of a plurality of worker threads based on the hierarchy of task group queues. Each of the worker threads is associated with a processor in the multi-processor system. The tasks of the task groups are executed via the worker threads according to the order in the hierarchy.

Abstract: Mechanisms and techniques are employed for managing the allocation and load balancing of storage system resources for the containerized, distributed execution of applications on a storage system. A control component executing on a processing component of the storage system may control reserving the necessary resources on one or more processing components to implement an application, and control a container management module to create, deploy and/or modify one or more containers on one or more processing components of the storage system. The one or more containers then may be executed to implement the application. Multiple processing components of the storage system may have a resource management module executing thereon. The control component may exchange communications with the one or more resource management modules of each processing component to determine the resources available within the processing component; e.g.

Abstract: In an approach for generating differentiated workload telemetry data, a processor corresponds one or more services with a workload related telemetry generating an event emitter. A processor performs a correlation analysis of corresponding relationship and connection among connected resources and current traffic into and out of the one or more services. A processor labels domain context for each telemetry event. A processor communicates each telemetry event to a global event handler. A processor performs a cross-correlation in real-time of telemetry data with the global event handler. A processor updates a real-time differentiated workload report.

Abstract: A computer implemented system is described for assigning executable jobs to pipeline sets, whereby the jobs may be network based computer jobs. The assigning includes generating a weight for each pipeline set of multiple pipeline sets to obtain multiple weights. Generating a weight includes obtaining duty cycle metrics for pipeline software threads in the pipeline set. The duty cycle metrics include a measure of an amount of time that a corresponding pipeline thread is executing and actively processing data. Generating the weight further includes determining the weight for the pipeline set based at least in part on the duty cycle metrics. The method further includes assigning a job request to a target pipeline set selected from the pipeline sets according to a weighted random algorithm, wherein the weighted random algorithm uses the weights.

Abstract: A visualization request to visualize data is received. System parameters responsive to the visualization request are obtained. Predicted performance metrics are generated with a machine learning model using the system parameters. A workload server is obtained using a workload server profile selected using the predicted performance metrics, the workload server executing a visualization workload to generate a visualization. The visualization of the data is streamed, from the workload server to the client device, responsive to the visualization request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scheduling operations represented on a computation graph. One of the methods receiving, by a computation graph system, a request to generate a schedule for processing a computation graph, obtaining data representing the computation graph generating a separator of the computation graph; and generating the schedule to perform the operations represented in the computation graph, wherein generating the schedule comprises: initializing the schedule with zero nodes; for each node in the separator: determining whether the node has any predecessor nodes in the computation graph, when the node has any predecessor nodes, adding the predecessor nodes to the schedule, and adding the node in the schedule, and adding to the schedule each node in each subgraph that is not a predecessor to any node in the separator on the computation graph.

Abstract: A scheduler manages execution of a plurality of data-collection jobs, assigns individual jobs to specific forwarders in a set of forwarders, and generates and transmits tokens (e.g., pairs of data-collection tasks and target sources) to assigned forwarders. The forwarder uses the tokens, along with stored information applicable across jobs, to collect data from the target source and forward it onto an indexer for processing. For example, the indexer can then break a data stream into discrete events, extract a timestamp from each event and index (e.g., store) the event based on the timestamp. The scheduler can monitor forwarders' job performance, such that it can use the performance to influence subsequent job assignments. Thus, data-collection jobs can be efficiently assigned to and executed by a group of forwarders, where the group can potentially be diverse and dynamic in size.

Abstract: An information handling system may include a processor and a basic input/output system configured to identify, test, and/or initialize information handling resources of the information handling system, and further configured to predict a volume of incoming telemetry data collected by a preboot driver of the basic input/output system and based on the volume predicted, manage storage of the telemetry data among memory associated with the basic input/output system.

Abstract: Methods and apparatus for flexible batch job scheduling in virtualization environments are disclosed. A descriptor for a batch job requested by a client is received at a job scheduling service. The descriptor comprises an indication of a time range during which a job iteration may be performed. A target time for executing the iteration is determined based on an analysis of a plurality of received descriptors. An indication of the target time at which the iteration is to be scheduled is provided to a selected execution platform.

Abstract: The technology disclosed herein enables a hypervisor to send a security policy to a virtual machine, which may use the security policy to validate system call invocations requested by a guest operating system. The system call invocations may be validated prior to being received by the hypervisor. The hypervisor may also validate system call invocations that are successfully validated by the virtual machine. An example method may include: identifying, by a hypervisor on a host machine, a security policy associated with a virtual machine, wherein the security policy specifies one or more validation rules, causing, by the hypervisor, the security policy to be imported into a guest operating system of the virtual machine from the hypervisor, and responsive to receiving, by the guest operating system, a first request to perform a system call, validating, by the guest operating system, the first request in accordance with the validation rules.

Abstract: A system can include a host device that includes a host management component and a virtual machine execution environment with a guest management component. The guest management component receives a data object generated by the host component. The data object specifies host parameters detected for the host device and hypervisor parameters detected for the hypervisor component. The hypervisor component relays the data object from the host management component to the guest management component, which identifies a violation of a compliance rule using this information. The guest management component performs an action based on the violation.

Abstract: A method of scheduling tasks within a GPU or other highly parallel processing unit is described which is both age-aware and wakeup event driven. Tasks which are received are added to an age-based task queue. Wakeup event bits for task types, or combinations of task types and data groups, are set in response to completion of a task dependency and these wakeup event bits are used to select an oldest task from the queue that satisfies predefined criteria.

Abstract: A method for creating a virtual machine includes: receiving a virtual machine creation request to create a plurality of virtual machines; dividing the plurality of virtual machines into a plurality of virtual machine groups; determining a home physical rack for each virtual machine group, where one virtual machine group corresponds to one home physical rack; and creating each virtual machine group on the home physical rack of each virtual machine group. Because each virtual machine group is created on a home physical rack to which each virtual machine group belongs, each virtual machine group is equivalent to one physical rack.

Abstract: One or more techniques and/or systems are disclosed for redeploying a baseline VM (BVM) to one or more child VMs (CVMs) by merely cloning virtual drives of the BVM, instead of the entirety of the parent BVM. A temporary directory is created in a datastore that has the target CVMs that are targeted for virtual drive replacement (e.g., are to be “re-baselined”). One or more replacement virtual drives (RVDs) are created in the temporary directory, where the RVDs comprise a clone of a virtual drive of the source BVM. The one or more RVDs are moved from the temporary directory to a directory of the target CVMs, replacing existing virtual drives of the target CVMs so that the target CVMs are thus re-baselined to the state of the parent BVM.

Abstract: Embodiments of the present invention provide a method, a system, and an apparatus for creating a virtual machine. The method includes: receiving a virtual machine creation request to create a plurality of virtual machines; dividing the plurality of virtual machines into a plurality of virtual machine groups; determining a home physical rack for each virtual machine group, where one virtual machine group corresponds to one home physical rack; and creating each virtual machine group on the home physical rack of each virtual machine group. Because each virtual machine group is created on a home physical rack to which each virtual machine group belongs, each virtual machine group is equivalent to one physical rack.